As the U.S. explores creative policies to address the needs of its growing elderly population, a better understanding of their effects on the individuals they are intended to benefit is needed. Our aims are: (1) To characterize the patterns of living arrangement trajectories experienced by disabled elderly persons. Longitudinal data on a cohort of disabled elderly persons with measures at 5 different points over a 10-year span will be used to characterize living arrangement trajectories; (2) To characterize combinations of state Medicaid long-term care (LTC) policies and to investigate the effects of the policy profiles in one's state of residence on patterns of living and care arrangements over time; and (3) To develop and estimate dynamic models of LTC service use and living arrangement choices. A wide array of "structural" and "performance" state LTC policies will be assembled and their effectiveness examined. Using multivariate and Classification and Regression Trees techniques, we propose to (i) to identify state LTC policy combinations that are internally homogeneous and that have similar expected outcomes in terms of living and care arrangement patterns; (ii) to develop an algorithm that will classify, states according to their closeness to identified policy combinations and (iii) to examine the effect of states classification with respective to the alternative combinations of policies to individuals' living and care arrangement choices over time. The empirical work will use panel data available for a nationally representative sample of elderly persons, the Assets and Health Dynamics of the Elderly. Results from these analyses will be used to simulate the effect of alternative state LTC policies on intensity and composition of care receipt (i.e., formal and informal care hours) and on living arrangement choices and transitions of elderly persons. Given the increasing numbers of elderly persons and the unresolved questions concerning the effectiveness of existing initiatives, the results of this study will have important implications for both policy and program development.